/* C <- alpha*A*B + beta*C */
void gemm(double alpha, Mat mA, Mat mB, double beta, Mat mC) {
const int n = MatN(mA);
const void* const a = MatElems(mA);
const void* const b = MatElems(mB);
void* const c = MatElems(mC);
const bool dev = MatDev(mA);
switch (MatElemSize(mA)) {
case 4:
if (dev) {
float alpha32 = alpha, beta32 = beta;
cublasSgemm(g_cublasHandle, CUBLAS_OP_N, CUBLAS_OP_N, n, n, n,
&alpha32, a, n, b, n, &beta32, c, n);
} else {
cblas_sgemm(CblasColMajor, CblasNoTrans, CblasNoTrans, n, n,
n, alpha, a, n, b, n, beta, c, n);
}
break;
case 8:
if (dev) {
cublasDgemm(g_cublasHandle, CUBLAS_OP_N, CUBLAS_OP_N, n, n, n,
&alpha, a, n, b, n, &beta, c, n);
} else {
cblas_dgemm(CblasColMajor, CblasNoTrans, CblasNoTrans, n, n,
n, alpha, a, n, b, n, beta, c, n);
}
break;
}
}
CAMLprim value spoc_cublasDgemm(value transa, value transb,
value m, value n, value k,
value alpha, value a, value lda,
value b, value ldb, value beta, value c, value ldc, value dev){
CAMLparam5(transa, transb, m, n, k);
CAMLxparam5(alpha, a, lda, b, ldb);
CAMLxparam4(beta, c, ldc, dev);
CAMLlocal3(dev_vec_array, dev_vec, gi);
CUdeviceptr d_A;
CUdeviceptr d_B;
CUdeviceptr d_C;
int id;
gi = Field(dev, 0);
id = Int_val(Field(gi, 7));
GET_VEC(a, d_A);
GET_VEC(b, d_B);
GET_VEC(c, d_C);
//CUBLAS_GET_CONTEXT;
CUBLAS_GET_CONTEXT;
cublasDgemm (Int_val(transa), Int_val(transb), Int_val(m), Int_val(n),
Int_val(k), (double)Double_val(alpha), (double*) d_A, Int_val(lda),
(double*) d_B, Int_val(ldb), (double) Double_val(beta),
(double *)d_C, Int_val(ldc));
CUDA_RESTORE_CONTEXT;
CAMLreturn(Val_unit);
}
void gpu_cublas1(double *A, double *B, double *C, double *D, double *r, double *nrmC, int N, int N2)
{
#pragma acc data present(A, B, C, D)
{
#pragma acc host_data use_device(A, B, C, D)
{
cublasHandle_t handle;
cublasCreate(&handle);
const double alpha = 1.0;
const double beta = 0.0;
cublasDgemm(handle, CUBLAS_OP_T, CUBLAS_OP_T, N, N, N, &alpha, A, N, B, N, &beta, C, N);
printf(" gpu gemm success \n");
cublasDdot(handle, N2, C, 1, B, 1, r);
printf(" gpu dot success \n");
*r = -1.0 * *r;
cublasDaxpy(handle, N2, r, B, 1, C, 1);
printf(" gpu axpy success \n");
cublasDnrm2(handle, N2, C, 1, nrmC);
printf(" gpu nrm2 success \n");
cublasDcopy(handle, N2, C, 1, D, 1);
printf(" gpu copy success \n");
*nrmC = 1.0 / *nrmC;
cublasDscal(handle, N2, nrmC, D, 1);
printf(" gpu scal success \n");
cublasDestroy(handle);
printf(" gpu destroy success \n");
}
}
}
static vl::Error
gemm(vl::Context& context,
char op1, char op2,
ptrdiff_t m, ptrdiff_t n, ptrdiff_t k,
type alpha,
type const * a, ptrdiff_t lda,
type const * b, ptrdiff_t ldb,
type beta,
type * c, ptrdiff_t ldc)
{
cublasHandle_t handle ;
cublasStatus_t status ;
status = context.getCudaHelper().getCublasHandle(&handle) ;
if (status != CUBLAS_STATUS_SUCCESS) goto done ;
status = cublasDgemm(handle,
(op1 == 't') ? CUBLAS_OP_T : CUBLAS_OP_N,
(op2 == 't') ? CUBLAS_OP_T : CUBLAS_OP_N,
(int)m, (int)n, (int)k,
&alpha,
a, (int)lda,
b, (int)ldb,
&beta,
c, (int)ldc);
done:
return context.setError
(context.getCudaHelper().catchCublasError(status, "cublasDgemm"), __func__) ;
}
void
cube_blas_d_gemm (cube_t *ctx,
cube_blas_op_t transa, cube_blas_op_t transb,
int m, int n, int k,
const double *alpha,
const double *A, int lda,
const double *B, int ldb,
const double *beta,
double *C, int ldc)
{
cublasStatus_t status;
cublasOperation_t ta, tb;
if (! cube_context_check (ctx))
return;
ta = (cublasOperation_t) transa;
tb = (cublasOperation_t) transb;
status = cublasDgemm (ctx->h_blas,
ta, tb,
m, n, k,
alpha,
A, lda,
B, ldb,
beta,
C, ldc);
cube_blas_check (ctx, status);
}
void d_gemm(SEXP rtransa, SEXP rtransb, SEXP ralpha, SEXP ra, SEXP rlda,
SEXP rb, SEXP rldb, SEXP rbeta, SEXP rc, SEXP rldc)
{
char
transa = getTranspose(rtransa),
transb = getTranspose(rtransb);
double
alpha = asReal(ralpha), beta = asReal(rbeta),
* a, * b, * c;
int
m, n, k,
rowsa, colsa, lda = asInteger(rlda),
rowsb, colsb, ldb = asInteger(rldb),
rowsc, colsc, ldc = asInteger(rldc);
unpackMatrix(ra, &rowsa, &colsa, &a);
unpackMatrix(rb, &rowsb, &colsb, &b);
unpackMatrix(rc, &rowsc, &colsc, &c);
m = rowsa;
n = colsb;
k = colsa;
if(isTranspose(transa)) {
m = colsa;
k = rowsa;
}
if(isTranspose(transb))
n = rowsb;
cublasDgemm(transa, transb, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc);
checkCublasError("d_gemm");
}
static int dgemm(cb_order order, cb_transpose transA, cb_transpose transB,
size_t M, size_t N, size_t K, double alpha,
gpudata *A, size_t offA, size_t lda,
gpudata *B, size_t offB, size_t ldb,
double beta, gpudata *C, size_t offC, size_t ldc) {
cuda_context *ctx = A->ctx;
gpudata *T;
size_t t;
cublasStatus_t err;
cb_transpose transT;
ASSERT_BUF(A);
ASSERT_BUF(B);
ASSERT_BUF(C);
if (order == cb_c) {
/* swap A and B */
t = N;
N = M;
M = t;
T = A;
A = B;
B = T;
t = lda;
lda = ldb;
ldb = t;
transT = transA;
transA = transB;
transB = transT;
t = offA;
offA = offB;
offB = t;
}
cuda_enter(ctx);
cuda_wait(A, CUDA_WAIT_READ);
cuda_wait(B, CUDA_WAIT_READ);
cuda_wait(C, CUDA_WAIT_READ|CUDA_WAIT_WRITE);
err = cublasDgemm(((blas_handle *)ctx->blas_handle)->h,
convT(transA), convT(transB), M, N, K,
&alpha, ((double *)A->ptr) + offA, lda,
((double *)B->ptr) + offB, ldb, &beta,
((double *)C->ptr) + offC, ldc);
if (err != CUBLAS_STATUS_SUCCESS) {
cuda_exit(ctx);
if (err == CUBLAS_STATUS_ARCH_MISMATCH)
return GA_DEVSUP_ERROR;
return GA_BLAS_ERROR;
}
cuda_record(A, CUDA_WAIT_READ);
cuda_record(B, CUDA_WAIT_READ);
cuda_record(C, CUDA_WAIT_READ|CUDA_WAIT_WRITE);
cuda_exit(ctx);
return GA_NO_ERROR;
}
inline void gemm( const Order order, const TransA transa, const TransB transb,
const int m, const int n, const int k, const double alpha,
const double* a, const int lda, const double* b, const int ldb,
const double beta, double* c, const int ldc ) {
BOOST_STATIC_ASSERT( (is_same<Order, tag::column_major>::value) );
cublasDgemm( blas_option< TransA >::value, blas_option< TransB >::value,
m, n, k, alpha, a, lda, b, ldb, beta, c, ldc );
}
void gemm(bool transa, bool transb, int m, int n, int k, double alpha, thrust::device_ptr<const double> A, int lda,
thrust::device_ptr<const double> B, int ldb, double beta, thrust::device_ptr<double> C, int ldc)
{
const cublasOperation_t ctransa = transa ? CUBLAS_OP_T : CUBLAS_OP_N;
const cublasOperation_t ctransb = transb ? CUBLAS_OP_T : CUBLAS_OP_N;
cublasSetStream(context::get().cublasHandle, context::get().stream);
cublasDgemm(context::get().cublasHandle, ctransa, ctransb, m, n, k, &alpha, A.get(), lda, B.get(), ldb, &beta, C.get(), ldc);
}
Darray<double> cudot (const Darray<double>& lhs, const Darray<double>& rhs)
{
// context check
CHECK_EQ(lhs.getDeviceManager().getDeviceID(), rhs.getDeviceManager().getDeviceID());
CHECK_EQ(lhs.ndim(), rhs.ndim());
CHECK_LT(lhs.ndim(), 3);
CHECK_LT(rhs.ndim(), 3);
Darray<double> ret;
if (lhs.ndim()==1 && rhs.ndim()==1)
{
// shape check
CHECK_EQ(lhs.size(), rhs.size());
ret = Darray<double>(lhs.getDeviceManager(), {1});
// using cublas ddot
lhs.deviceSet();
cublasDdot (DeviceManager::handle,
lhs.size(),
lhs.data,
1,
rhs.data,
1,
ret.data);
}
// 2D matrix dot
else if (lhs.ndim()==2 && rhs.ndim()==2)
{
// shape check
CHECK_EQ(lhs.shape()[1], rhs.shape()[0]);
ret = Darray<double>(lhs.getDeviceManager(), {lhs.shape()[0], rhs.shape()[1]});
// using cblas dgemm
lhs.deviceSet();
const double alpha = 1.;
const double beta = 0.;
CUBLAS_SAFE_CALL(
cublasDgemm (DeviceManager::handle,
CUBLAS_OP_N,
CUBLAS_OP_N,
lhs.shape()[0],
rhs.shape()[1],
lhs.shape()[1],
&alpha,
lhs.dev_data,
lhs.shape()[0],
rhs.dev_data,
rhs.shape()[0],
&beta,
ret.dev_data,
ret.shape()[0])
);
}
return ret;
}
void contractTensor(cublasHandle_t &handle, sTensorGPU &tensorIn1, sTensorGPU &tensorIn2, sTensorGPU &tensorOut, int numDim = 1)
{
//printf("\nContract(%d) ->%d\n", numDim, tensorIn2.id);
//printTensor(tensorIn1);
//printTensor(tensorIn2);
//sTensor tensorOut;
tensorOut.dim = tensorIn1.dim + tensorIn2.dim - numDim * 2;
//tensorOut.size = (int*)malloc(sizeof(int)*tensorOut.dim);
int outW = 1;
int outH = 1;
int contract = tensorIn1.size[0];
int test = tensorIn2.size[0];
for (int i = 1; i<numDim; i++){
contract *= tensorIn1.size[i];
test *= tensorIn2.size[i];
}
if (test != contract){
printf("Unequal Size %d!=%d\n", contract, test);
}
{
int i = 0;
for (int j = numDim; j<tensorIn1.dim; j++){
tensorOut.size[i++] = tensorIn1.size[j];
outW *= tensorIn1.size[j];
}
for (int j = numDim; j<tensorIn2.dim; j++){
tensorOut.size[i++] = tensorIn2.size[j];
outH *= tensorIn2.size[j];
}
}
tensorOut.dataSize = outW * outH;
//double* deviceData;
//handleError(cudaMalloc((void **)&deviceData, sizeof(double)*tensorOut.dataSize));
//tensorOut.deviceData = deviceData;
type alpha = 1.f;
type beta = 0.f;
cublasStatus_t ret;
double flops = outW;
flops *= outH;
flops *= contract;
flops *= 2;
bops += flops;
// m n k lda x k ldA ldb x n ldB ldc x n ldC
ret = cublasDgemm(handle, CUBLAS_OP_T, CUBLAS_OP_N, outW, outH, contract, &alpha, tensorIn1.deviceData, contract, tensorIn2.deviceData, contract, &beta, tensorOut.deviceData, outW);
if (ret != CUBLAS_STATUS_SUCCESS)
{
printf("cublasSgemm returned error code %d\n", ret);
}
cudaDeviceSynchronize();
//freeTensor(tensorIn1);
//freeTensor(tensorIn2);
//return tensorOut;
}
void caffe_gpu_gemm<double>(const CBLAS_TRANSPOSE TransA,
const CBLAS_TRANSPOSE TransB, const int M, const int N, const int K,
const double alpha, const double* A, const double* B, const double beta,
double* C) {
// Note that cublas follows fortran order.
int lda = (TransA == CblasNoTrans) ? K : M;
int ldb = (TransB == CblasNoTrans) ? N : K;
cublasOperation_t cuTransA =
(TransA == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T;
cublasOperation_t cuTransB =
(TransB == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T;
CUBLAS_CHECK(cublasDgemm(Caffe::cublas_handle(), cuTransB, cuTransA,
N, M, K, &alpha, B, ldb, A, lda, &beta, C, N));
}
// Note : cublasDgemm( handle, CUBLAS_OP_N, CUBLAS_OP_N, n,n,n, &alpha, A, n, B, n, &beta, C, n)
// means matrix C = B * A
void cublas_gemm(int n, double *c, double *b, double *a )
{
#pragma acc data present(a, b, c)
{
#pragma acc host_data use_device(a, b, c)
{
cublasHandle_t handle;
cublasCreate(&handle);
const double alpha = 1.0;
const double beta = 0.0;
cublasDgemm( handle, CUBLAS_OP_N, CUBLAS_OP_N, n,n,n, &alpha, a, n, b, n, &beta, c, n);
cublasDestroy(handle);
}
}
}
void cublas_gemm(const double *A, const double *B, double *C, int N)
{
#pragma acc data present(A, B, C)
{
#pragma acc host_data use_device(A, B, C)
{
cublasHandle_t h;
cublasCreate(&h);
const double alpha = 1.0;
const double beta = 0.0;
cublasDgemm(h, CUBLAS_OP_T, CUBLAS_OP_T, N, N, N, &alpha, A, N, B, N, &beta, C, N);
cublasDestroy(h);
}
}
}
void magma_dgemm(
magma_trans_t transA, magma_trans_t transB,
magma_int_t m, magma_int_t n, magma_int_t k,
double alpha, double const* dA, magma_int_t lda,
double const* dB, magma_int_t ldb,
double beta, double* dC, magma_int_t ldc )
{
cublasDgemm(
cublas_trans_const( transA ),
cublas_trans_const( transB ),
m, n, k,
alpha, dA, lda,
dB, ldb,
beta, dC, ldc );
}
static void GEMM(Teuchos::ETransp transA, Teuchos::ETransp transB, double alpha,
View<const double**,LayoutLeft,Cuda> A, View<const double**,LayoutLeft,Cuda> B,
double beta, View<double**,LayoutLeft,Cuda> C) {
const int m = static_cast<int>(C.dimension_0()),
n = static_cast<int>(C.dimension_1()),
k = (transA == Teuchos::NO_TRANS ? A.dimension_1() : A.dimension_0()),
lda = static_cast<int>(Impl::getStride2DView(A)),
ldb = static_cast<int>(Impl::getStride2DView(B)),
ldc = static_cast<int>(Impl::getStride2DView(C));
const char char_transA = (transA == Teuchos::NO_TRANS ? 'N' : 'T'),
char_transB = (transB == Teuchos::NO_TRANS ? 'N' : 'T');
cublasDgemm(char_transA, char_transB, m, n, k, alpha, A.ptr_on_device(), lda, B.ptr_on_device(), ldb, beta, C.ptr_on_device(), ldc);
#ifdef HAVE_KOKKOS_DEBUG
cublasStatus info = cublasGetError();
TEUCHOS_TEST_FOR_EXCEPTION( info != CUBLAS_STATUS_SUCCESS, std::runtime_error, "cublasDgemm failed with status " << info << "." );
#endif
}
// Multiply the arrays A and B on GPU and save the result in C
// C(m,n) = A(m,k) * B(k,n)
void gpu_blas_mmul(const double *A, const double *B, double *C, const int m, const int k, const int n) {
int lda=m,ldb=k,ldc=m;
const double alf = 1;
const double bet = 0;
const double *alpha = &alf;
const double *beta = &bet;
// Create a handle for CUBLAS
cublasHandle_t handle;
cublasCreate(&handle);
// Do the actual multiplication
cublasDgemm(handle, CUBLAS_OP_N, CUBLAS_OP_N, m, n, k, alpha, A, lda, B, ldb, beta, C, ldc);
// Destroy the handle
cublasDestroy(handle);
}
double cublas_gemm_norm(const double *A, const double *B, double *C, int N)
{
double *norm;
norm = (double *) malloc(1*sizeof(double));
#pragma acc data present(A, B, C) copyout(norm[0])
{
#pragma acc host_data use_device(A, B, C)
{
cublasHandle_t h;
cublasCreate(&h);
const double alpha = 1.0;
const double beta = 0.0;
cublasDgemm(h, CUBLAS_OP_T, CUBLAS_OP_T, N, N, N, &alpha, A, N, B, N, &beta, C, N);
cublasDnrm2(h, N*N, C, 1, norm);
cublasDestroy(h);
}
}
return *norm;
}
int tiramisu_cublas_dgemm(double *A, double *B, double *C,
uint64_t M, uint64_t N, uint64_t K,
double alpha, double beta,
uint64_t ldA, uint64_t ldB, uint64_t ldC,
uint64_t offsetA, uint64_t offsetB, uint64_t offsetC,
bool transposeA, bool transposeB)
{
// TODO: Destroy the handle.
static bool handle_created = false;
static cublasHandle_t handle;
if (!handle_created) {
cublasCreate(&handle);
handle_created = true;
}
// Default values for tight packing:
if (ldA == 0) {
ldA = transposeA ? M : K;
}
if (ldB == 0) {
ldB = transposeB ? K : N;
}
if (ldC == 0) {
ldC = N;
}
// The cuBLAS GEMM accepts column major buffers by default. We do a simple
// trick here to multiply row major matrices. From a row-major perspective,
// column-major multiplication basically transposes inputs, multiplies, and
// transposes the output again: cublas(A, B) = ((A^T)x(B^T))^T = BxA
// So it is actually equivalent to row-major GEMM with inputs swapped.
// We need to reorder the size parameters as well to make it work:
cublasSetStream(handle, cudaStreamPerThread);
handle_cublas_error(
cublasDgemm(handle,
transposeB ? CUBLAS_OP_T : CUBLAS_OP_N,
transposeA ? CUBLAS_OP_T : CUBLAS_OP_N,
N, M, K,
&alpha, B + offsetB, ldB, A + offsetA, ldA,
&beta, C + offsetC, ldC),
__FUNCTION__);
return 0;
}
SEXP magMultmm(SEXP a, SEXP transa, SEXP b, SEXP transb)
{
SEXP gpu = magGetGPU(a, b),
c = PROTECT(NEW_OBJECT(MAKE_CLASS("magma")));
int TA = LOGICAL_VALUE(transa), TB = LOGICAL_VALUE(transb),
*DIMA = INTEGER(GET_DIM(a)), *DIMB = INTEGER(GET_DIM(b)),
M = DIMA[TA], N = DIMB[!TB], K = DIMA[!TA],
LDA = DIMA[0], LDB = DIMB[0], LDC = M;
char TRANSA = (TA ? 'T' : 'N'), TRANSB = (TB ? 'T' : 'N');
double *A = REAL(PROTECT(AS_NUMERIC(a))), *B = REAL(PROTECT(AS_NUMERIC(b))),
*dA, *dB, *dC;
if(DIMB[TB] != K) error("non-conformable matrices");
c = SET_SLOT(c, install(".Data"), allocMatrix(REALSXP, M, N));
SET_SLOT(c, install("gpu"), duplicate(gpu));
magma_malloc((void**)&dA, (M*K)*sizeof(double));
magma_malloc((void**)&dB, (K*N)*sizeof(double));
magma_malloc((void**)&dC, (M*N)*sizeof(double));
magma_dsetmatrix(DIMA[0], DIMA[1], A, LDA, dA, LDA);
magma_dsetmatrix(DIMB[0], DIMB[1], B, LDB, dB, LDB);
if(LOGICAL_VALUE(gpu))
magmablas_dgemm(TRANSA, TRANSB, M, N, K, 1.0, dA, LDA, dB, LDB, 0.0, dC, LDC);
else
cublasDgemm(TRANSA, TRANSB, M, N, K, 1.0, dA, LDA, dB, LDB, 0.0, dC, LDC);
magma_dgetmatrix(M, N, dC, LDC, REAL(c), LDC);
magma_free(dA);
magma_free(dB);
magma_free(dC);
UNPROTECT(3);
return c;
}
void mat_prod_mat(const double* a, cublasOperation_t op_a, const double* b, cublasOperation_t op_b, double*c, int m, int n, int k){
cudaError_t cudaStat ; // cudaMalloc status
cublasStatus_t stat ; // CUBLAS functions status
cublasHandle_t handle ; // CUBLAS context
// on the device
double* d_a; // d_a - a on the device
double* d_b; // d_b - b on the device
double* d_c; // d_c - c on the device
cudaStat = cudaMalloc((void **)&d_a ,m*k*sizeof(*a)); // device
// memory alloc for a
cudaStat = cudaMalloc((void **)&d_b ,k*n*sizeof(*b)); // device
// memory alloc for b
cudaStat = cudaMalloc((void **)&d_c ,m*n*sizeof(*c)); // device
// memory alloc for c
stat = cublasCreate(&handle); // initialize CUBLAS context
// copy matrices from the host to the device
stat = cublasSetMatrix (m,k, sizeof(*a) ,a,m,d_a ,m); //a -> d_a
stat = cublasSetMatrix (k,n, sizeof(*b) ,b,k,d_b ,k); //b -> d_b
stat = cublasSetMatrix (m,n, sizeof(*c) ,c,m,d_c ,m); //c -> d_c
double al=1.0;
double bet=1.0;
// matrix - matrix multiplication : d_c = al*d_a *d_b + bet *d_c
// d_a -mxk matrix , d_b -kxn matrix , d_c -mxn matrix ;
// al ,bet -scalars
stat=cublasDgemm(handle,op_a,op_b,m,n,k,&al,d_a,m,d_b,k,&bet,d_c,m);
stat = cublasGetMatrix (m, n, sizeof(*c) ,d_c ,m,c,m); // cp d_c - >c
cudaFree (d_a ); // free device memory
cudaFree (d_b ); // free device memory
cudaFree (d_c ); // free device memory
cublasDestroy ( handle ); // destroy CUBLAS context
}
cublasStatus_t cublasXgemm(cublasOperation_t transa, cublasOperation_t transb, int m, int n, int k, const double *alpha,
const double *A, int lda, const double *B, int ldb, const double *beta, double *C, int ldc) {
return cublasDgemm(g_context->cublasHandle, transa, transb, m, n, k, alpha, A, lda, B, ldb, beta, C, ldc);
}
int main( int argc, char** argv )
{
magma_init();
cublasHandle_t handle;
cudaSetDevice( 0 );
cublasCreate( &handle );
double *A, *B, *C;
double *dA, *dB, *dC;
double error, work[1];
double c_one = MAGMA_D_ONE;
double c_neg_one = MAGMA_D_NEG_ONE;
magma_int_t ione = 1;
magma_int_t ISEED[4] = { 1, 2, 3, 4 };
magma_int_t n = 10;
magma_int_t lda = n;
magma_int_t ldda = ((n+31)/32)*32;
magma_int_t size = lda*n;
magma_int_t info;
magma_dmalloc_cpu( &A, lda*n );
magma_dmalloc_cpu( &B, lda*n );
magma_dmalloc_cpu( &C, lda*n );
magma_dmalloc( &dA, ldda*n );
magma_dmalloc( &dB, ldda*n );
magma_dmalloc( &dC, ldda*n );
// initialize matrices
lapackf77_dlarnv( &ione, ISEED, &size, A );
lapackf77_dlarnv( &ione, ISEED, &size, B );
lapackf77_dlarnv( &ione, ISEED, &size, C );
// increase diagonal to be SPD
for( int i=0; i < n; ++i ) {
C[i+i*lda] = MAGMA_D_ADD( C[i+i*lda], MAGMA_D_MAKE( n*n, 0 ));
}
magma_dsetmatrix( n, n, A, lda, dA, ldda );
magma_dsetmatrix( n, n, B, lda, dB, ldda );
magma_dsetmatrix( n, n, C, lda, dC, ldda );
// compute with cublas
cublasDgemm( handle, CUBLAS_OP_N, CUBLAS_OP_N, n, n, n,
&c_neg_one, dA, ldda, dB, ldda, &c_one, dC, ldda );
magma_dpotrf_gpu( MagmaLower, n, dC, ldda, &info );
if (info != 0)
printf("magma_dpotrf returned error %d: %s.\n",
(int) info, magma_strerror( info ));
// compute with LAPACK
blasf77_dgemm( MagmaNoTransStr, MagmaNoTransStr, &n, &n, &n,
&c_neg_one, A, &lda, B, &lda, &c_one, C, &lda );
lapackf77_dpotrf( MagmaLowerStr, &n, C, &lda, &info );
if (info != 0)
printf("lapackf77_dpotrf returned error %d: %s.\n",
(int) info, magma_strerror( info ));
// compute difference
magma_dgetmatrix( n, n, dC, ldda, A, lda );
blasf77_daxpy( &size, &c_neg_one, C, &ione, A, &ione );
error = lapackf77_dlange( "F", &n, &n, A, &lda, work );
printf( "n %d, error %8.2e\n", n, error );
magma_free( dA );
magma_free( dB );
magma_free( dC );
magma_free_cpu( A );
magma_free_cpu( B );
magma_free_cpu( C );
cublasDestroy( handle );
magma_finalize();
return 0;
}
static int dgemmBatch(cb_order order, cb_transpose transA, cb_transpose transB,
size_t M, size_t N, size_t K, double alpha,
gpudata **A, size_t *offA, size_t lda,
gpudata **B, size_t *offB, size_t ldb,
double beta, gpudata **C, size_t *offC, size_t ldc,
size_t batchCount) {
cuda_context *ctx;
size_t *lt, t;
gpudata **T;
size_t i;
cb_transpose transT;
cublasStatus_t err;
if (batchCount == 0) return GA_NO_ERROR;
ASSERT_BUF(A[0]);
ctx = A[0]->ctx;
cuda_enter(ctx);
if (order == cb_c) {
/* swap A and B */
t = N;
N = M;
M = t;
T = A;
A = B;
B = T;
t = lda;
lda = ldb;
ldb = t;
transT = transA;
transA = transB;
transB = transT;
lt = offA;
offA = offB;
offB = lt;
}
// use parallel cublasSgemm calls rather than cublasSgemmBatched for large products
const size_t threshold = 650;
const int multiple_dispatch = M * N * K > threshold * threshold * threshold;
if (multiple_dispatch) {
for (i = 0; i < batchCount; i++) {
ASSERT_BUF(A[i]);
ASSERT_BUF(B[i]);
ASSERT_BUF(C[i]);
cuda_wait(A[i], CUDA_WAIT_READ);
cuda_wait(B[i], CUDA_WAIT_READ);
cuda_wait(C[i], CUDA_WAIT_READ|CUDA_WAIT_WRITE);
err = cublasDgemm(((blas_handle *)ctx->blas_handle)->h,
convT(transA), convT(transB),
M, N, K, &alpha,
(double*)A[i]->ptr + offA[i], lda,
(double*)B[i]->ptr + offB[i], ldb,
&beta,
(double*)C[i]->ptr + offC[i], ldc);
if (err != CUBLAS_STATUS_SUCCESS) {
cuda_exit(ctx);
if (err == CUBLAS_STATUS_ARCH_MISMATCH)
return GA_DEVSUP_ERROR;
return GA_BLAS_ERROR;
}
cuda_record(A[i], CUDA_WAIT_READ);
cuda_record(B[i], CUDA_WAIT_READ);
cuda_record(C[i], CUDA_WAIT_READ|CUDA_WAIT_WRITE);
}
} else {
double **T_l = alloca(sizeof(double *) * batchCount * 3);
const double **A_l = (const double **)T_l;
const double **B_l = (const double **)T_l + batchCount;
double **C_l = T_l + (batchCount * 2);
CUdeviceptr Ta, Aa, Ba, Ca;
for (i = 0; i < batchCount; i++) {
ASSERT_BUF(A[i]);
ASSERT_BUF(B[i]);
ASSERT_BUF(C[i]);
cuda_wait(A[i], CUDA_WAIT_READ);
cuda_wait(B[i], CUDA_WAIT_READ);
cuda_wait(C[i], CUDA_WAIT_READ|CUDA_WAIT_WRITE);
A_l[i] = ((double *)A[i]->ptr) + offA[i];
B_l[i] = ((double *)B[i]->ptr) + offB[i];
C_l[i] = ((double *)C[i]->ptr) + offC[i];
}
cuMemAlloc(&Ta, sizeof(double *) * batchCount * 3);
Aa = Ta;
Ba = Ta + (batchCount * sizeof(double *));
Ca = Ta + (batchCount * sizeof(double *) * 2);
cuMemcpyHtoD(Ta, T_l, sizeof(double *) * batchCount * 3);
err = cublasDgemmBatched(((blas_handle *)ctx->blas_handle)->h,
convT(transA), convT(transB),
M, N, K, &alpha, (const double **)Aa, lda,
(const double **)Ba, ldb, &beta,
(double **)Ca, ldc, batchCount);
cuMemFree(Ta);
if (err != CUBLAS_STATUS_SUCCESS) {
cuda_exit(ctx);
if (err == CUBLAS_STATUS_ARCH_MISMATCH)
return GA_DEVSUP_ERROR;
return GA_BLAS_ERROR;
}
for (i = 0; i < batchCount; i++) {
cuda_record(A[i], CUDA_WAIT_READ);
cuda_record(B[i], CUDA_WAIT_READ);
cuda_record(C[i], CUDA_WAIT_READ|CUDA_WAIT_WRITE);
}
}
cuda_exit(ctx);
return GA_NO_ERROR;
}
int cublas_multiply_matrix_double2( PGM_Matriz_Double *A, PGM_Matriz_Double *B, PGM_Matriz_Double *result, PGM_Matriz_Double *work){
cublasStatus_t status;
cublasHandle_t handle;
int max_dim = work->n_linhas;
PGM_Matriz_GPU device_A,
device_B,
device_C;
double alpha = 1.0,
beta = 0.0;
if (A->n_linhas != result->n_linhas || B->n_colunas != result->n_colunas){
return 0;
}
status = cublasCreate(&handle);
if (status != CUBLAS_STATUS_SUCCESS){
return -1;
}
if(create_PGM_Matriz_GPU_double(&device_A,A->n_linhas, A->n_colunas,max_dim) != cudaSuccess){
return -2;
}
if(create_PGM_Matriz_GPU_double(&device_B,B->n_linhas, B->n_colunas,max_dim) != cudaSuccess){
if(cudaFree(device_A.valor) == cudaSuccess) return -2;
else return -11;
}
if(create_PGM_Matriz_GPU_double(&device_C,result->n_linhas, result->n_colunas,max_dim) != cudaSuccess){
if(cudaFree(device_A.valor) == cudaSuccess && cudaFree(device_B.valor) == cudaSuccess) return -2;
else return -11;
}
if(double_copyMatrixHost2GPU(A,work,&device_A) != CUBLAS_STATUS_SUCCESS){
if(cudaFree(device_A.valor) == cudaSuccess && cudaFree(device_B.valor) == cudaSuccess && cudaFree(device_C.valor) == cudaSuccess) return -3;
else return -12;
}
if(double_copyMatrixHost2GPU(B,work,&device_B) != CUBLAS_STATUS_SUCCESS){
if(cudaFree(device_A.valor) == cudaSuccess && cudaFree(device_B.valor) == cudaSuccess && cudaFree(device_C.valor) == cudaSuccess) return -3;
else return -12;
}
if(cublasDgemm(handle, CUBLAS_OP_T, CUBLAS_OP_T, max_dim,max_dim,max_dim, &alpha,(double*) device_A.valor,max_dim,(double*)device_B.valor, max_dim, &beta, (double*)device_C.valor, max_dim) != CUBLAS_STATUS_SUCCESS){
if(cudaFree(device_A.valor) == cudaSuccess && cudaFree(device_B.valor) == cudaSuccess && cudaFree(device_C.valor) == cudaSuccess) return -4;
else return -13;
}
if(double_copyMatrixGPU2Host_Transpose(result, &device_C, work) != CUBLAS_STATUS_SUCCESS){
if(cudaFree(device_A.valor) == cudaSuccess && cudaFree(device_B.valor) == cudaSuccess && cudaFree(device_C.valor) == cudaSuccess) return -5;
else return -14;
}
if(cudaFree(device_A.valor) != cudaSuccess){
return -15;
}
if(cudaFree(device_B.valor) != cudaSuccess){
return -15;
}
if(cudaFree(device_C.valor) != cudaSuccess){
return -15;
}
if(cublasDestroy(handle) != CUBLAS_STATUS_SUCCESS){
return -7;
}
return 1;
}
int main( int argc, char** argv )
{
TESTING_INIT();
real_Double_t gflops, t1, t2;
double c_neg_one = MAGMA_D_NEG_ONE;
magma_int_t ione = 1;
const char trans[] = { 'N', 'C', 'T' };
const char uplo[] = { 'L', 'U' };
const char diag[] = { 'U', 'N' };
const char side[] = { 'L', 'R' };
double *A, *B, *C, *C2, *LU;
double *dA, *dB, *dC1, *dC2;
double alpha = MAGMA_D_MAKE( 0.5, 0.1 );
double beta = MAGMA_D_MAKE( 0.7, 0.2 );
double dalpha = 0.6;
double dbeta = 0.8;
double work[1], error, total_error;
magma_int_t ISEED[4] = {0,0,0,1};
magma_int_t m, n, k, size, maxn, ld, info;
magma_int_t *piv;
magma_err_t err;
magma_opts opts;
parse_opts( argc, argv, &opts );
printf( "Compares magma wrapper function to cublas function; all diffs should be exactly 0.\n\n" );
total_error = 0.;
for( int i = 0; i < opts.ntest; ++i ) {
m = opts.msize[i];
n = opts.nsize[i];
k = opts.ksize[i];
printf("=========================================================================\n");
printf( "M %d, N %d, K %d\n", (int) m, (int) n, (int) k );
// allocate matrices
// over-allocate so they can be any combination of {m,n,k} x {m,n,k}.
maxn = max( max( m, n ), k );
ld = maxn;
size = maxn*maxn;
err = magma_malloc_cpu( (void**) &piv, maxn*sizeof(magma_int_t) ); assert( err == 0 );
err = magma_dmalloc_pinned( &A, size ); assert( err == 0 );
err = magma_dmalloc_pinned( &B, size ); assert( err == 0 );
err = magma_dmalloc_pinned( &C, size ); assert( err == 0 );
err = magma_dmalloc_pinned( &C2, size ); assert( err == 0 );
err = magma_dmalloc_pinned( &LU, size ); assert( err == 0 );
err = magma_dmalloc( &dA, size ); assert( err == 0 );
err = magma_dmalloc( &dB, size ); assert( err == 0 );
err = magma_dmalloc( &dC1, size ); assert( err == 0 );
err = magma_dmalloc( &dC2, size ); assert( err == 0 );
// initialize matrices
size = maxn*maxn;
lapackf77_dlarnv( &ione, ISEED, &size, A );
lapackf77_dlarnv( &ione, ISEED, &size, B );
lapackf77_dlarnv( &ione, ISEED, &size, C );
printf( "========== Level 1 BLAS ==========\n" );
// ----- test DSWAP
// swap 2nd and 3rd columns of dA, then copy to C2 and compare with A
assert( n >= 4 );
magma_dsetmatrix( m, n, A, ld, dA, ld );
magma_dsetmatrix( m, n, A, ld, dB, ld );
magma_dswap( m, dA(0,1), 1, dA(0,2), 1 );
magma_dswap( m, dB(0,1), 1, dB(0,2), 1 );
// check results, storing diff between magma and cuda calls in C2
cublasDaxpy( ld*n, c_neg_one, dA, 1, dB, 1 );
magma_dgetmatrix( m, n, dB, ld, C2, ld );
error = lapackf77_dlange( "F", &m, &k, C2, &ld, work );
total_error += error;
printf( "dswap diff %.2g\n", error );
// ----- test IDAMAX
// get argmax of column of A
magma_dsetmatrix( m, k, A, ld, dA, ld );
error = 0;
for( int j = 0; j < k; ++j ) {
magma_int_t i1 = magma_idamax( m, dA(0,j), 1 );
magma_int_t i2 = cublasIdamax( m, dA(0,j), 1 );
assert( i1 == i2 );
error += abs( i1 - i2 );
}
total_error += error;
gflops = (double)m * k / 1e9;
printf( "idamax diff %.2g\n", error );
printf( "\n" );
printf( "========== Level 2 BLAS ==========\n" );
// ----- test DGEMV
// c = alpha*A*b + beta*c, with A m*n; b,c m or n-vectors
// try no-trans/trans
for( int ia = 0; ia < 3; ++ia ) {
magma_dsetmatrix( m, n, A, ld, dA, ld );
magma_dsetvector( maxn, B, 1, dB, 1 );
magma_dsetvector( maxn, C, 1, dC1, 1 );
magma_dsetvector( maxn, C, 1, dC2, 1 );
t1 = magma_sync_wtime( 0 );
magma_dgemv( trans[ia], m, n, alpha, dA, ld, dB, 1, beta, dC1, 1 );
t1 = magma_sync_wtime( 0 ) - t1;
t2 = magma_sync_wtime( 0 );
cublasDgemv( trans[ia], m, n, alpha, dA, ld, dB, 1, beta, dC2, 1 );
t2 = magma_sync_wtime( 0 ) - t2;
// check results, storing diff between magma and cuda call in C2
size = (trans[ia] == 'N' ? m : n);
cublasDaxpy( size, c_neg_one, dC1, 1, dC2, 1 );
magma_dgetvector( size, dC2, 1, C2, 1 );
error = lapackf77_dlange( "F", &size, &ione, C2, &ld, work );
total_error += error;
gflops = FLOPS_DGEMV( m, n ) / 1e9;
printf( "dgemv( %c ) diff %.2g, Gflop/s %6.2f, %6.2f\n",
trans[ia], error, gflops/t1, gflops/t2 );
}
printf( "\n" );
// ----- test DSYMV
// c = alpha*A*b + beta*c, with A m*m symmetric; b,c m-vectors
// try upper/lower
for( int iu = 0; iu < 2; ++iu ) {
magma_dsetmatrix( m, m, A, ld, dA, ld );
magma_dsetvector( m, B, 1, dB, 1 );
magma_dsetvector( m, C, 1, dC1, 1 );
magma_dsetvector( m, C, 1, dC2, 1 );
t1 = magma_sync_wtime( 0 );
magma_dsymv( uplo[iu], m, alpha, dA, ld, dB, 1, beta, dC1, 1 );
t1 = magma_sync_wtime( 0 ) - t1;
t2 = magma_sync_wtime( 0 );
cublasDsymv( uplo[iu], m, alpha, dA, ld, dB, 1, beta, dC2, 1 );
t2 = magma_sync_wtime( 0 ) - t2;
// check results, storing diff between magma and cuda call in C2
cublasDaxpy( m, c_neg_one, dC1, 1, dC2, 1 );
magma_dgetvector( m, dC2, 1, C2, 1 );
error = lapackf77_dlange( "F", &m, &ione, C2, &ld, work );
total_error += error;
gflops = FLOPS_DSYMV( m ) / 1e9;
printf( "dsymv( %c ) diff %.2g, Gflop/s %6.2f, %6.2f\n",
uplo[iu], error, gflops/t1, gflops/t2 );
}
printf( "\n" );
// ----- test DTRSV
// solve A*c = c, with A m*m triangular; c m-vector
// try upper/lower, no-trans/trans, unit/non-unit diag
// Factor A into LU to get well-conditioned triangles, else solve yields garbage.
// Still can give garbage if solves aren't consistent with LU factors,
// e.g., using unit diag for U, so copy lower triangle to upper triangle.
// Also used for trsm later.
lapackf77_dlacpy( "Full", &maxn, &maxn, A, &ld, LU, &ld );
lapackf77_dgetrf( &maxn, &maxn, LU, &ld, piv, &info );
for( int j = 0; j < maxn; ++j ) {
for( int i = 0; i < j; ++i ) {
*LU(i,j) = *LU(j,i);
}
}
for( int iu = 0; iu < 2; ++iu ) {
for( int it = 0; it < 3; ++it ) {
for( int id = 0; id < 2; ++id ) {
magma_dsetmatrix( m, m, LU, ld, dA, ld );
magma_dsetvector( m, C, 1, dC1, 1 );
magma_dsetvector( m, C, 1, dC2, 1 );
t1 = magma_sync_wtime( 0 );
magma_dtrsv( uplo[iu], trans[it], diag[id], m, dA, ld, dC1, 1 );
t1 = magma_sync_wtime( 0 ) - t1;
t2 = magma_sync_wtime( 0 );
cublasDtrsv( uplo[iu], trans[it], diag[id], m, dA, ld, dC2, 1 );
t2 = magma_sync_wtime( 0 ) - t2;
// check results, storing diff between magma and cuda call in C2
cublasDaxpy( m, c_neg_one, dC1, 1, dC2, 1 );
magma_dgetvector( m, dC2, 1, C2, 1 );
error = lapackf77_dlange( "F", &m, &ione, C2, &ld, work );
total_error += error;
gflops = FLOPS_DTRSM( MagmaLeft, m, 1 ) / 1e9;
printf( "dtrsv( %c, %c, %c ) diff %.2g, Gflop/s %6.2f, %6.2f\n",
uplo[iu], trans[it], diag[id], error, gflops/t1, gflops/t2 );
}}}
printf( "\n" );
printf( "========== Level 3 BLAS ==========\n" );
// ----- test DGEMM
// C = alpha*A*B + beta*C, with A m*k or k*m; B k*n or n*k; C m*n
// try combinations of no-trans/trans
for( int ia = 0; ia < 3; ++ia ) {
for( int ib = 0; ib < 3; ++ib ) {
bool nta = (trans[ia] == 'N');
bool ntb = (trans[ib] == 'N');
magma_dsetmatrix( (nta ? m : k), (nta ? m : k), A, ld, dA, ld );
magma_dsetmatrix( (ntb ? k : n), (ntb ? n : k), B, ld, dB, ld );
magma_dsetmatrix( m, n, C, ld, dC1, ld );
magma_dsetmatrix( m, n, C, ld, dC2, ld );
t1 = magma_sync_wtime( 0 );
magma_dgemm( trans[ia], trans[ib], m, n, k, alpha, dA, ld, dB, ld, beta, dC1, ld );
t1 = magma_sync_wtime( 0 ) - t1;
t2 = magma_sync_wtime( 0 );
cublasDgemm( trans[ia], trans[ib], m, n, k, alpha, dA, ld, dB, ld, beta, dC2, ld );
t2 = magma_sync_wtime( 0 ) - t2;
// check results, storing diff between magma and cuda call in C2
cublasDaxpy( ld*n, c_neg_one, dC1, 1, dC2, 1 );
magma_dgetmatrix( m, n, dC2, ld, C2, ld );
error = lapackf77_dlange( "F", &m, &n, C2, &ld, work );
total_error += error;
gflops = FLOPS_DGEMM( m, n, k ) / 1e9;
printf( "dgemm( %c, %c ) diff %.2g, Gflop/s %6.2f, %6.2f\n",
trans[ia], trans[ib], error, gflops/t1, gflops/t2 );
}}
printf( "\n" );
// ----- test DSYMM
// C = alpha*A*B + beta*C (left) with A m*m symmetric; B,C m*n; or
// C = alpha*B*A + beta*C (right) with A n*n symmetric; B,C m*n
// try left/right, upper/lower
for( int is = 0; is < 2; ++is ) {
for( int iu = 0; iu < 2; ++iu ) {
magma_dsetmatrix( m, m, A, ld, dA, ld );
magma_dsetmatrix( m, n, B, ld, dB, ld );
magma_dsetmatrix( m, n, C, ld, dC1, ld );
magma_dsetmatrix( m, n, C, ld, dC2, ld );
t1 = magma_sync_wtime( 0 );
magma_dsymm( side[is], uplo[iu], m, n, alpha, dA, ld, dB, ld, beta, dC1, ld );
t1 = magma_sync_wtime( 0 ) - t1;
t2 = magma_sync_wtime( 0 );
cublasDsymm( side[is], uplo[iu], m, n, alpha, dA, ld, dB, ld, beta, dC2, ld );
t2 = magma_sync_wtime( 0 ) - t2;
// check results, storing diff between magma and cuda call in C2
cublasDaxpy( ld*n, c_neg_one, dC1, 1, dC2, 1 );
magma_dgetmatrix( m, n, dC2, ld, C2, ld );
error = lapackf77_dlange( "F", &m, &n, C2, &ld, work );
total_error += error;
gflops = FLOPS_DSYMM( side[is], m, n ) / 1e9;
printf( "dsymm( %c, %c ) diff %.2g, Gflop/s %6.2f, %6.2f\n",
side[is], uplo[iu], error, gflops/t1, gflops/t2 );
}}
printf( "\n" );
// ----- test DSYRK
// C = alpha*A*A^H + beta*C (no-trans) with A m*k and C m*m symmetric; or
// C = alpha*A^H*A + beta*C (trans) with A k*m and C m*m symmetric
// try upper/lower, no-trans/trans
for( int iu = 0; iu < 2; ++iu ) {
for( int it = 0; it < 3; ++it ) {
magma_dsetmatrix( n, k, A, ld, dA, ld );
magma_dsetmatrix( n, n, C, ld, dC1, ld );
magma_dsetmatrix( n, n, C, ld, dC2, ld );
t1 = magma_sync_wtime( 0 );
magma_dsyrk( uplo[iu], trans[it], n, k, dalpha, dA, ld, dbeta, dC1, ld );
t1 = magma_sync_wtime( 0 ) - t1;
t2 = magma_sync_wtime( 0 );
cublasDsyrk( uplo[iu], trans[it], n, k, dalpha, dA, ld, dbeta, dC2, ld );
t2 = magma_sync_wtime( 0 ) - t2;
// check results, storing diff between magma and cuda call in C2
cublasDaxpy( ld*n, c_neg_one, dC1, 1, dC2, 1 );
magma_dgetmatrix( n, n, dC2, ld, C2, ld );
error = lapackf77_dlange( "F", &n, &n, C2, &ld, work );
total_error += error;
gflops = FLOPS_DSYRK( k, n ) / 1e9;
printf( "dsyrk( %c, %c ) diff %.2g, Gflop/s %6.2f, %6.2f\n",
uplo[iu], trans[it], error, gflops/t1, gflops/t2 );
}}
printf( "\n" );
// ----- test DSYR2K
// C = alpha*A*B^H + ^alpha*B*A^H + beta*C (no-trans) with A,B n*k; C n*n symmetric; or
// C = alpha*A^H*B + ^alpha*B^H*A + beta*C (trans) with A,B k*n; C n*n symmetric
// try upper/lower, no-trans/trans
for( int iu = 0; iu < 2; ++iu ) {
for( int it = 0; it < 3; ++it ) {
bool nt = (trans[it] == 'N');
magma_dsetmatrix( (nt ? n : k), (nt ? n : k), A, ld, dA, ld );
magma_dsetmatrix( n, n, C, ld, dC1, ld );
magma_dsetmatrix( n, n, C, ld, dC2, ld );
t1 = magma_sync_wtime( 0 );
magma_dsyr2k( uplo[iu], trans[it], n, k, alpha, dA, ld, dB, ld, dbeta, dC1, ld );
t1 = magma_sync_wtime( 0 ) - t1;
t2 = magma_sync_wtime( 0 );
cublasDsyr2k( uplo[iu], trans[it], n, k, alpha, dA, ld, dB, ld, dbeta, dC2, ld );
t2 = magma_sync_wtime( 0 ) - t2;
// check results, storing diff between magma and cuda call in C2
cublasDaxpy( ld*n, c_neg_one, dC1, 1, dC2, 1 );
magma_dgetmatrix( n, n, dC2, ld, C2, ld );
error = lapackf77_dlange( "F", &n, &n, C2, &ld, work );
total_error += error;
gflops = FLOPS_DSYR2K( k, n ) / 1e9;
printf( "dsyr2k( %c, %c ) diff %.2g, Gflop/s %6.2f, %6.2f\n",
uplo[iu], trans[it], error, gflops/t1, gflops/t2 );
}}
printf( "\n" );
// ----- test DTRMM
// C = alpha*A*C (left) with A m*m triangular; C m*n; or
// C = alpha*C*A (right) with A n*n triangular; C m*n
// try left/right, upper/lower, no-trans/trans, unit/non-unit
for( int is = 0; is < 2; ++is ) {
for( int iu = 0; iu < 2; ++iu ) {
for( int it = 0; it < 3; ++it ) {
for( int id = 0; id < 2; ++id ) {
bool left = (side[is] == 'L');
magma_dsetmatrix( (left ? m : n), (left ? m : n), A, ld, dA, ld );
magma_dsetmatrix( m, n, C, ld, dC1, ld );
magma_dsetmatrix( m, n, C, ld, dC2, ld );
t1 = magma_sync_wtime( 0 );
magma_dtrmm( side[is], uplo[iu], trans[it], diag[id], m, n, alpha, dA, ld, dC1, ld );
t1 = magma_sync_wtime( 0 ) - t1;
t2 = magma_sync_wtime( 0 );
cublasDtrmm( side[is], uplo[iu], trans[it], diag[id], m, n, alpha, dA, ld, dC2, ld );
t2 = magma_sync_wtime( 0 ) - t2;
// check results, storing diff between magma and cuda call in C2
cublasDaxpy( ld*n, c_neg_one, dC1, 1, dC2, 1 );
magma_dgetmatrix( m, n, dC2, ld, C2, ld );
error = lapackf77_dlange( "F", &n, &n, C2, &ld, work );
total_error += error;
gflops = FLOPS_DTRMM( side[is], m, n ) / 1e9;
printf( "dtrmm( %c, %c ) diff %.2g, Gflop/s %6.2f, %6.2f\n",
uplo[iu], trans[it], error, gflops/t1, gflops/t2 );
}}}}
printf( "\n" );
// ----- test DTRSM
// solve A*X = alpha*B (left) with A m*m triangular; B m*n; or
// solve X*A = alpha*B (right) with A n*n triangular; B m*n
// try left/right, upper/lower, no-trans/trans, unit/non-unit
for( int is = 0; is < 2; ++is ) {
for( int iu = 0; iu < 2; ++iu ) {
for( int it = 0; it < 3; ++it ) {
for( int id = 0; id < 2; ++id ) {
bool left = (side[is] == 'L');
magma_dsetmatrix( (left ? m : n), (left ? m : n), LU, ld, dA, ld );
magma_dsetmatrix( m, n, C, ld, dC1, ld );
magma_dsetmatrix( m, n, C, ld, dC2, ld );
t1 = magma_sync_wtime( 0 );
magma_dtrsm( side[is], uplo[iu], trans[it], diag[id], m, n, alpha, dA, ld, dC1, ld );
t1 = magma_sync_wtime( 0 ) - t1;
t2 = magma_sync_wtime( 0 );
cublasDtrsm( side[is], uplo[iu], trans[it], diag[id], m, n, alpha, dA, ld, dC2, ld );
t2 = magma_sync_wtime( 0 ) - t2;
// check results, storing diff between magma and cuda call in C2
cublasDaxpy( ld*n, c_neg_one, dC1, 1, dC2, 1 );
magma_dgetmatrix( m, n, dC2, ld, C2, ld );
error = lapackf77_dlange( "F", &n, &n, C2, &ld, work );
total_error += error;
gflops = FLOPS_DTRSM( side[is], m, n ) / 1e9;
printf( "dtrsm( %c, %c ) diff %.2g, Gflop/s %6.2f, %6.2f\n",
uplo[iu], trans[it], error, gflops/t1, gflops/t2 );
}}}}
printf( "\n" );
// cleanup
magma_free_cpu( piv );
magma_free_pinned( A );
magma_free_pinned( B );
magma_free_pinned( C );
magma_free_pinned( C2 );
magma_free_pinned( LU );
magma_free( dA );
magma_free( dB );
magma_free( dC1 );
magma_free( dC2 );
}
if ( total_error != 0. ) {
printf( "total error %.2g -- ought to be 0 -- some test failed (see above).\n",
total_error );
}
else {
printf( "all tests passed\n" );
}
TESTING_FINALIZE();
return 0;
}
int main( int argc, char **argv )
{
double *A, *B, *C;
double *cu_A, *cu_B, *cu_C;
cudaError_t cuError;
cublasStatus_t cuStatus;
cublasHandle_t cuHandle;
// seed rand()
srand(time(NULL));
// allocate memory on CPU
A = (double*)malloc(sizeof(double)*MATRIX_N*MATRIX_P);
B = (double*)malloc(sizeof(double)*MATRIX_P*MATRIX_M);
C = (double*)malloc(sizeof(double)*MATRIX_N*MATRIX_M);
if( !A || !B || !C )
{
perror("Can't allocate CPU matrices");
exit(EXIT_FAILURE);
}
// generate matrices
for( int i = 0; i < MATRIX_N*MATRIX_P; i++ )
A[i] = 10.0*((double)rand())/RAND_MAX;
for( int i = 0; i < MATRIX_P*MATRIX_M; i++ )
B[i] = 10.0*((double)rand())/RAND_MAX;
// allocate memory on GPU
cuError = cudaMalloc( &cu_A, sizeof(double)*MATRIX_N*MATRIX_P );
if( cuError != cudaSuccess )
{
fprintf(stderr, "Can't allocate GPU matrices\n");
exit(EXIT_FAILURE);
}
cuError = cudaMalloc( &cu_B, sizeof(double)*MATRIX_P*MATRIX_M );
if( cuError != cudaSuccess )
{
fprintf(stderr, "Can't allocate GPU matrices\n");
exit(EXIT_FAILURE);
}
cuError = cudaMalloc( &cu_C, sizeof(double)*MATRIX_N*MATRIX_M );
if( cuError != cudaSuccess )
{
fprintf(stderr, "Can't allocate GPU matrices\n");
exit(EXIT_FAILURE);
}
// setup cuBlas
cuStatus = cublasCreate( &cuHandle );
if( cuStatus != CUBLAS_STATUS_SUCCESS )
{
fprintf(stderr, "Error initializing cuBlas\n");
exit(EXIT_FAILURE);
}
// setup matrices
cuStatus = cublasSetMatrix( MATRIX_N, MATRIX_P, sizeof(double), A, MATRIX_N, cu_A, MATRIX_N );
if( cuStatus != CUBLAS_STATUS_SUCCESS )
{
fprintf(stderr, "Error transferring matrix A\n");
exit(EXIT_FAILURE);
}
cuStatus = cublasSetMatrix( MATRIX_P, MATRIX_M, sizeof(double), B, MATRIX_P, cu_B, MATRIX_P );
if( cuStatus != CUBLAS_STATUS_SUCCESS )
{
fprintf(stderr, "Error transferring matrix B\n");
exit(EXIT_FAILURE);
}
// multiply
double one = 1.0;
double zero = 0.0;
cuStatus = cublasDgemm( cuHandle, CUBLAS_OP_N, CUBLAS_OP_N, MATRIX_N, MATRIX_M, MATRIX_P, &one, cu_A, MATRIX_N, cu_B, MATRIX_P, &zero, cu_C, MATRIX_N );
if( cuStatus != CUBLAS_STATUS_SUCCESS )
{
fprintf(stderr, "Error executing matrix mult\n");
exit(EXIT_FAILURE);
}
// get results
cuStatus = cublasGetMatrix( MATRIX_N, MATRIX_M, sizeof(double), cu_C, MATRIX_N, C, MATRIX_N );
if( cuStatus != CUBLAS_STATUS_SUCCESS )
{
fprintf(stderr, "Error transferring results\n");
exit(EXIT_FAILURE);
}
// check results
bool good = true;
for( int i = 0; i < MATRIX_N; i++ )
{
for( int j = 0; j < MATRIX_M; j++ )
{
double sum = 0.0;
for( int k = 0; k < MATRIX_P; k++ )
{
sum += A[IDX2C(i, k, MATRIX_N)]*B[IDX2C(k, j, MATRIX_P)];
}
// check
if( fabs(sum - C[IDX2C(i,j,MATRIX_N)]) > 0.00001 )
{
good = false;
printf("(%i, %i) sum = %f\tcu_C = %f\tMISMATCH\n", i, j, sum, C[IDX2C(i,j,MATRIX_N)]);
}
}
}
if( good )
printf("Results Match\n");
else
printf("Results DO NOT Match\n");
// cleanup
free( A ); free( B ); free( C );
cudaFree( cu_A ); cudaFree( cu_B ); cudaFree( cu_C );
cublasDestroy( cuHandle );
return 0;
}
SEXP magma_dgeMatrix_matrix_mm(SEXP a, SEXP bP, SEXP right)
{
#ifdef HIPLAR_WITH_MAGMA
SEXP b = PROTECT(mMatrix_as_dgeMatrix(bP)),
val = PROTECT(NEW_OBJECT(MAKE_CLASS("dgeMatrix")));
int *adims = INTEGER(GET_SLOT(a, Matrix_DimSym)),
*bdims = INTEGER(GET_SLOT(b, Matrix_DimSym)),
*cdims = INTEGER(ALLOC_SLOT(val, Matrix_DimSym, INTSXP, 2));
double one = 1.0, zero = 0.0;
if (asLogical(right)) {
int m = bdims[0], n = adims[1], k = bdims[1];
if (adims[0] != k)
error(_("Matrices are not conformable for multiplication"));
cdims[0] = m; cdims[1] = n;
if (m < 1 || n < 1 || k < 1) {
// This was commented out
error(_("Matrices with zero extents cannot be multiplied"));
ALLOC_SLOT(val, Matrix_xSym, REALSXP, m * n);
} else {
double *B = REAL(GET_SLOT(b, Matrix_xSym));
double *A = REAL(GET_SLOT(a, Matrix_xSym));
double *C = REAL(ALLOC_SLOT(val, Matrix_xSym, REALSXP, m * n));
//TODO add magma here too
if(GPUFlag == 1) {
double *d_A, *d_B, *d_C;
cublasStatus retStatus;
#ifdef HIPLAR_DBG
R_ShowMessage("DBG: Performing matrix multiplication with Right = true using magmablas_dgemm");
#endif
cublasAlloc(n * k, sizeof(double), (void**)&d_A);
/* Error Checking */
retStatus = cublasGetError ();
if (retStatus != CUBLAS_STATUS_SUCCESS)
error(_("CUBLAS: Error in Memory Allocation"));
/********************************************/
cublasAlloc(m * k, sizeof(double), (void**)&d_B);
/* Error Checking */
retStatus = cublasGetError ();
if (retStatus != CUBLAS_STATUS_SUCCESS)
error(_("CUBLAS: Error in Memory Allocation"));
/********************************************/
cublasAlloc(m * n, sizeof(double), (void**)&d_C);
/* Error Checking */
retStatus = cublasGetError ();
if (retStatus != CUBLAS_STATUS_SUCCESS)
error(_("CUBLAS: Error in Memory Allocation"));
/********************************************/
cublasSetVector( n * k , sizeof(double), A, 1, d_A, 1);
/* Error Checking */
retStatus = cublasGetError ();
if (retStatus != CUBLAS_STATUS_SUCCESS)
error(_("CUBLAS: Error in Data Transfer to Device"));
/********************************************/
cublasSetVector( m * k, sizeof(double), B, 1, d_B, 1 );
/* Error Checking */
retStatus = cublasGetError ();
if (retStatus != CUBLAS_STATUS_SUCCESS)
error(_("CUBLAS: Error in Data Transfer to Device"));
/********************************************/
// ******** magmablas_dgemm call Here **
//magmablas_dgemm('N', 'N', m, n, k, one, d_B, m, d_A, k, zero, d_C, m);
//CHANGED 30/07
cublasDgemm('N', 'N', m, n, k, one, d_B, m, d_A, k, zero, d_C, m);
/* Error Checking */
retStatus = cublasGetError ();
if (retStatus != CUBLAS_STATUS_SUCCESS) {
error(_("CUBLAS: Error in cublasDgemm routine"));
}
/********************************************/
cublasGetVector( m * n , sizeof(double), d_C, 1, C, 1);
/* Error Checking */
retStatus = cublasGetError ();
if (retStatus != CUBLAS_STATUS_SUCCESS)
error(_("CUBLAS: Error in Data Transfer from Device"));
/********************************************/
cublasFree(d_A);
cublasFree(d_B);
cublasFree(d_C);
}
else {
#ifdef HIPLAR_DBG
R_ShowMessage("DBG: Performing matrix multiplication using dgemm with right = TRUE");
#endif
F77_CALL(dgemm) ("N", "N", &m, &n, &k, &one,
B, &m, A , &k, &zero, C , &m);
}
}
} else {
int m = adims[0], n = bdims[1], k = adims[1];
double *A = REAL(GET_SLOT(a, Matrix_xSym));
double *B = REAL(GET_SLOT(b, Matrix_xSym));
if (bdims[0] != k)
error(_("Matrices are not conformable for multiplication"));
cdims[0] = m; cdims[1] = n;
double *C = REAL(ALLOC_SLOT(val, Matrix_xSym, REALSXP, m * n));
if (m < 1 || n < 1 || k < 1) {
// This was commented out
error(_("Matrices with zero extents cannot be multiplied"));
ALLOC_SLOT(val, Matrix_xSym, REALSXP, m * n);
} else {
if(GPUFlag == 1) {
double *d_A, *d_B, *d_C;
cublasStatus retStatus;
#ifdef HIPLAR_DBG
R_ShowMessage("DBG: Performing matrix multiplication using magmablas_dgemm");
#endif
cublasAlloc(m * k, sizeof(double), (void**)&d_A);
/* Error Checking */
retStatus = cublasGetError ();
if (retStatus != CUBLAS_STATUS_SUCCESS)
error(_("CUBLAS: Error in Memory Allocation"));
/********************************************/
cublasAlloc(n * k, sizeof(double), (void**)&d_B);
/* Error Checking */
retStatus = cublasGetError ();
if (retStatus != CUBLAS_STATUS_SUCCESS)
error(_("CUBLAS: Error in Memory Allocation"));
/********************************************/
cublasAlloc(m * n, sizeof(double), (void**)&d_C);
/* Error Checking */
retStatus = cublasGetError ();
if (retStatus != CUBLAS_STATUS_SUCCESS)
error(_("CUBLAS: Error in Memory Allocation"));
/********************************************/
cublasSetVector( m * k , sizeof(double), A, 1, d_A, 1);
/* Error Checking */
retStatus = cublasGetError ();
if (retStatus != CUBLAS_STATUS_SUCCESS)
error(_("CUBLAS: Error in Data Transfer to Device"));
/********************************************/
cublasSetVector( n * k, sizeof(double), B, 1, d_B, 1 );
/* Error Checking */
retStatus = cublasGetError ();
if (retStatus != CUBLAS_STATUS_SUCCESS)
error(_("CUBLAS: Error in Data Transfer to Device"));
/********************************************/
// ******** magmablas_dgemm call Here **
//magmablas_dgemm('N', 'N', m, n, k, one, d_A, m, d_B, k, zero, d_C, m);
//CHANGE
cublasDgemm('N', 'N', m, n, k, one, d_A, m, d_B, k, zero, d_C, m);
retStatus = cublasGetError ();
if (retStatus != CUBLAS_STATUS_SUCCESS) {
error(_("CUBLAS: Error in Data Transfer from Device"));
/********************************************/
}
cublasGetVector( m * n , sizeof(double), d_C, 1, C, 1);
/* Error Checking */
retStatus = cublasGetError ();
if (retStatus != CUBLAS_STATUS_SUCCESS)
error(_("CUBLAS: Error in Data Transfer from Device"));
/********************************************/
cublasFree(d_A);
cublasFree(d_B);
cublasFree(d_C);
}
else {
#ifdef HIPLAR_DBG
R_ShowMessage("DBG: Performing matrix multiplication using dgemm");
#endif
F77_CALL(dgemm) ("N", "N", &m, &n, &k, &one,
A, &m,
B, &k, &zero,
C,
&m);
}
}
}
ALLOC_SLOT(val, Matrix_DimNamesSym, VECSXP, 2);
UNPROTECT(2);
return val;
#endif
return R_NilValue;
}
SEXP magma_dgeMatrix_matrix_crossprod(SEXP x, SEXP y, SEXP trans)
{
#ifdef HIPLAR_WITH_MAGMA
int tr = asLogical(trans);/* trans=TRUE: tcrossprod(x,y) */
SEXP val = PROTECT(NEW_OBJECT(MAKE_CLASS("dgeMatrix")));
int *xDims = INTEGER(GET_SLOT(x, Matrix_DimSym)),
*yDims = INTEGER(getAttrib(y, R_DimSymbol)),
*vDims, nprot = 1;
int m = xDims[!tr], n = yDims[!tr];/* -> result dim */
int xd = xDims[ tr], yd = yDims[ tr];/* the conformable dims */
double one = 1.0, zero = 0.0;
if (isInteger(y)) {
y = PROTECT(coerceVector(y, REALSXP));
nprot++;
}
if (!(isMatrix(y) && isReal(y)))
error(_("Argument y must be a numeric matrix"));
SET_SLOT(val, Matrix_factorSym, allocVector(VECSXP, 0));
SET_SLOT(val, Matrix_DimSym, allocVector(INTSXP, 2));
vDims = INTEGER(GET_SLOT(val, Matrix_DimSym));
if (xd > 0 && yd > 0 && n > 0 && m > 0) {
if (xd != yd)
error(_("Dimensions of x and y are not compatible for %s"),
tr ? "tcrossprod" : "crossprod");
vDims[0] = m; vDims[1] = n;
SET_SLOT(val, Matrix_xSym, allocVector(REALSXP, m * n));
double *A = REAL(GET_SLOT(x, Matrix_xSym));
double *B = REAL(y);
double *C = REAL(GET_SLOT(val, Matrix_xSym));
if(GPUFlag == 1) {
double *d_A, *d_B, *d_C;
cublasStatus retStatus;
#ifdef HIPLAR_DBG
R_ShowMessage("DBG: Performing dge/matrix crossprod using magmablas_dgemm");
#endif
cublasAlloc(m * xd, sizeof(double), (void**)&d_A);
/* Error Checking */
retStatus = cublasGetError ();
if (retStatus != CUBLAS_STATUS_SUCCESS)
error(_("CUBLAS: Error in Memory Allocation"));
/********************************************/
cublasAlloc(n * xd, sizeof(double), (void**)&d_B);
/* Error Checking */
retStatus = cublasGetError ();
if (retStatus != CUBLAS_STATUS_SUCCESS)
error(_("CUBLAS: Error in Memory Allocation"));
/********************************************/
cublasAlloc(m * n, sizeof(double), (void**)&d_C);
/* Error Checking */
retStatus = cublasGetError ();
if (retStatus != CUBLAS_STATUS_SUCCESS)
error(_("CUBLAS: Error in Memory Allocation"));
/********************************************/
cublasSetVector( m * xd , sizeof(double), A, 1, d_A, 1);
/* Error Checking */
retStatus = cublasGetError ();
if (retStatus != CUBLAS_STATUS_SUCCESS)
error(_("CUBLAS: Error in Data Transfer to Device"));
/********************************************/
cublasSetVector( xd * n, sizeof(double), B, 1, d_B, 1 );
/* Error Checking */
retStatus = cublasGetError ();
if (retStatus != CUBLAS_STATUS_SUCCESS)
error(_("CUBLAS: Error in Data Transfer to Device"));
/********************************************/
cublasSetVector( m * n, sizeof(double), C, 1, d_C, 1 );
/* Error Checking */
retStatus = cublasGetError ();
if (retStatus != CUBLAS_STATUS_SUCCESS)
error(_("CUBLAS: Error in Data Transfer to Device"));
/********************************************/
// ******** magmablas_dgemm call Here **
//magmablas_dgemm( tr ? 'N' : 'T', tr ? 'T' : 'N', m, n, xd, one, d_A, xDims[0], d_B, yDims[0], zero, d_C, m);
//CHANGE
cublasDgemm( tr ? 'N' : 'T', tr ? 'T' : 'N', m, n, xd, one, d_A, xDims[0], d_B, yDims[0], zero, d_C, m);
cublasGetVector( m * n , sizeof(double), d_C, 1, C, 1);
/* Error Checking */
retStatus = cublasGetError ();
if (retStatus != CUBLAS_STATUS_SUCCESS)
error(_("CUBLAS: Error in Data Transfer from Device"));
/********************************************/
cublasFree(d_A);
cublasFree(d_B);
cublasFree(d_C);
}
else {
#ifdef HIPLAR_DBG
R_ShowMessage("DBG: Performing dge/matrix cross prod with dgemm");
#endif
F77_CALL(dgemm)(tr ? "N" : "T", tr ? "T" : "N", &m, &n, &xd, &one,
A , xDims,
B , yDims,
&zero, C, &m);
}
}
UNPROTECT(nprot);
return val;
#endif
return R_NilValue;
}
void blasx_gpu_dgemm_kernel(int j,
int nrowa, int ncola,
int nrowb, int ncolb,
int nrowc, int ncolc,
int current_task, int prior_task,
enum CBLAS_TRANSPOSE TransA, enum CBLAS_TRANSPOSE TransB,
double* A, double* B, double* C,
int lda, int ldb, int ldc,
int x, int y, int z,
double** C_dev,
cudaStream_t *stream, cublasHandle_t *handle_p,
int current_stream,
double alpha, double beta, int block_dim,
int switcher, int* task_batch_counter,
LRU_t **LRUs, int GPUs,
int *mem_cpy_counter,
reader_tracker *addr_track,
int GPU_id)
{
int nrowa_dev, nrowb_dev, nrowc_dev;
int ncola_dev, ncolb_dev, ncolc_dev;
int nrow_offset_a, nrow_offset_b;
int ncol_offset_a, ncol_offset_b;
int i = current_task/(y+1);
int k = current_task%(y+1);
double *A_dev, *B_dev;
if (TransA != CblasNoTrans) {
margin_adjustment(nrowa,ncola,block_dim,j,i,&nrowa_dev,&ncola_dev);
}else{
margin_adjustment(nrowa,ncola,block_dim,i,j,&nrowa_dev,&ncola_dev);
}
if (TransB != CblasNoTrans) {
margin_adjustment(nrowb,ncolb,block_dim,k,j,&nrowb_dev,&ncolb_dev);
}else{
margin_adjustment(nrowb,ncolb,block_dim,j,k,&nrowb_dev,&ncolb_dev);
}
margin_adjustment(nrowc,ncolc,block_dim,i,k,&nrowc_dev,&ncolc_dev);
if (TransA != CblasNoTrans) {
nrow_offset_a = j*block_dim, ncol_offset_a = i*block_dim;
}else{
nrow_offset_a = i*block_dim, ncol_offset_a = j*block_dim;
}
if (TransB != CblasNoTrans) {
nrow_offset_b = k*block_dim, ncol_offset_b = j*block_dim;
}else{
nrow_offset_b = j*block_dim, ncol_offset_b = k*block_dim;
}
double *starting_point_A = &A[nrow_offset_a+ncol_offset_a*lda];
double *starting_point_B = &B[nrow_offset_b+ncol_offset_b*ldb];
//Asynchonizing set matrix on GPU
//----------------LRU&RBT optimization----------------//
mem_control_kernel_double(starting_point_A, &A_dev, LRUs, GPUs, GPU_id, block_dim, mem_cpy_counter, addr_track, stream, nrowa_dev, ncola_dev, lda);
mem_control_kernel_double(starting_point_B, &B_dev, LRUs, GPUs, GPU_id, block_dim, mem_cpy_counter, addr_track, stream, nrowb_dev, ncolb_dev, ldb);
//----------------------------------------------------//
if (j == 0) {
margin_adjustment(nrowc,ncolc,block_dim,i,k,&nrowc_dev,&ncolc_dev);
int nrow_offset_c = i*block_dim;
int ncol_offset_c = k*block_dim;
double *starting_point_C = &C[nrow_offset_c+ncol_offset_c*ldc];
if (beta != 0) {
assert( cublasSetMatrixAsync(nrowc_dev, ncolc_dev, sizeof(double), starting_point_C, ldc, C_dev[switcher*STREAMNUM+current_stream], block_dim, *stream) == CUBLAS_STATUS_SUCCESS );
}
if (*task_batch_counter != 0) {//Set matrix back
int i_pre = prior_task/(y+1);
int k_pre = prior_task%(y+1);
int nrowc_dev_pre, ncolc_dev_pre;
margin_adjustment(nrowc,ncolc,block_dim,i_pre,k_pre,&nrowc_dev_pre,&ncolc_dev_pre);
int nrow_offset_c_pre = i_pre*block_dim;
int ncol_offset_c_pre = k_pre*block_dim;
double *starting_point_C_pre = &C[nrow_offset_c_pre+ncol_offset_c_pre*ldc];
assert( cublasGetMatrixAsync(nrowc_dev_pre, ncolc_dev_pre, sizeof(double), C_dev[current_stream+(1-switcher)*STREAMNUM], block_dim, starting_point_C_pre, ldc,*stream) == CUBLAS_STATUS_SUCCESS);
}
}
cudaStreamSynchronize(*stream);
assert( cublasSetStream(*handle_p, *stream) == CUBLAS_STATUS_SUCCESS );
double beta_inner = (j==0)?(beta):(1);
int ka = (TransA != CblasNoTrans)?(nrowa_dev):(ncola_dev);
cublasOperation_t transa, transb;
CBLasTransToCuBlasTrans(TransA, &transa);
CBLasTransToCuBlasTrans(TransB, &transb);
cublasStatus_t status = cublasDgemm(*handle_p,
transa, transb,
nrowc_dev, ncolc_dev, ka,
&alpha,
A_dev, block_dim,
B_dev, block_dim,
&beta_inner,
C_dev[switcher*STREAMNUM+current_stream], block_dim);
assert( status == CUBLAS_STATUS_SUCCESS );
}
